基于BP神经网络算法的铝合金黏度预测模型

黏度是液体铝合金重要的热物性质之一,影响到液体充型与凝固的特征,继而铸件的质量。在本研究中,从文献中收集了315组实验测定的黏度数据,用来开发黏度预测模型。采用BP神经网络算法构建模型,以熔体温度和合金中Al、Si、Fe、Cu、Mn、Mg和Zn的含量作为模型输入,并以黏度值作为模型输出。为了改善模型精度,研究不同训练算法和隐含层神经元数的影响。使用遗传算法优化初始权重与赋值,这显著改善了模型精度。模型预测值与实验值间的相对误差小于5%,证明所构建的优化模型具有高的预测精度与可靠性。用建立的模型对纯Al和二元Al合金的黏度随温度和溶质含量变化的预测结果与文献中的实验结果非常一致,表明该模型在工程应用中具有较好的预测精度。

Effect of Ce on castability,mechanical properties and electric conductivity of commercial purity aluminum

Effects of Ce addition on microstructure,castability(fluidity and hot tearing sensitivity),mechanical properties and electric conductivity of commercial purity aluminum(CP-AI) were investigated through microstructure observation and performance tests.Results show that adding Ce in a CP-AI can considerably refine the grains,and has an important influence on the amount,crystallographic forms,and distribution of secondary phases.Addition of Ce also has a large impact on the fluidity and hot tearing sensitivity(HTS) of the CP-AI.With the addition of Ce from 0.1wt.%to 0.5wt.%,the fluidity of CP-AI is first increased remarkably and then decreased,and the HTS has an opposite response.The best castability of the studied alloys appears to be at 0.2wt.%-0.3wt.%Ce addition.The remarkable improvement in castability is attributed to the considerable refinement of grain structure.Ce addition can also lead to a significant rise in electric conductivity.The maximum conductivity of the as-cast CP-AI is 59.7%IACS with an addition of 0.2wt.%Ce.The T7 heat treatment can further improves the conductivity to 60.7%IACS.The Ce-induced evolution of the secondary phases is believed to be the mechanism for it.

Microstructure and mechanical properties of lost foam cast 356 alloys

Microstructure and mechanical properties of lost foam cast aluminum alloys have been investigated in both primary A356(0.13% Fe) and secondary 356(0.47%). As expected, secondary 356 shows much higher content of Fe-rich intermetallic phases, and in particular the porosity in comparison with primary A356. The average area percent and size(length) of Fe-rich intermetallics change from about 0.5% and 6 μm in A356 to 2% and 25 μm in 356 alloy. The average area percent and maximum size of porosity also increase from about 0.4% and 420 μm to 1.4% and 600 μm, respectively. As a result, tensile ductility decreases about 60% and ultimate tensile strength declines about 8%. Lower fatigue strength was also experienced in the secondary 356 alloy. Low cycle fatigue(LCF) strength decreased from 187 MPa in A356 to 159 MPa in 356 and high cycle fatigue(HCF) strength also declined slightly from 68 MPa to 64 MPa.

Quantitative microstructure and fatigue life of B319 casting alloys

In this study, the microstructure of B319 casting alloys and effects of five different casting conditions on microstructure were studied. Multi-scale microstructure was quantified in terms of secondary dendrite arm spacing （SDAS）, and Si particle size and aspect ratio. The effects of SDAS, Si aspect ratio and size on fatigue life were analyzed. The results indicate that the size and aspect ratio of Si particles are a function of SDAS which is dependent on cooling rate during solidification. The fatigue life decreases with SDAS increasing as SDAS is smaller than 30 pm while it increases with SDAS increasing as SDAS is larger than 60 ~tm. In addition, the fatigue life decreases with Si aspect ratio and size increasing at the same SDAS. Moreover, SDAS and Si particles have also influence on fatigue fracture, such as the area of cracks propagation region and the roughness of fatigue fracture. The cracks propagation area is smaller, and the fatigue fracture is similar to tensile fracture with larger SDAS. Besides, the longitudinal section of fatigue fracture is rougher with large SDAS and elongated Si particles.